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The R Book Hardcover – June 19, 2007

ISBN-13: 978-0470510247 ISBN-10: 0470510242 Edition: 1st

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Product Details

  • Hardcover: 950 pages
  • Publisher: Wiley; 1 edition (June 19, 2007)
  • Language: English
  • ISBN-10: 0470510242
  • ISBN-13: 978-0470510247
  • Product Dimensions: 6.8 x 2.2 x 9.8 inches
  • Shipping Weight: 3.7 pounds
  • Average Customer Review: 3.7 out of 5 stars  See all reviews (44 customer reviews)
  • Amazon Best Sellers Rank: #740,029 in Books (See Top 100 in Books)

Editorial Reviews

Amazon.com Review

The high-level language of R is recognized as one of the most powerful and flexible statistical software environments, and is rapidly becoming the standard setting for quantitative analysis, statistics and graphics. R provides free access to unrivalled coverage and cutting-edge applications, enabling the user to apply numerous statistical methods ranging from simple regression to time series or multivariate analysis.

Building on the success of the author’s bestselling Statistics: An Introduction using R, The R Book is packed with worked examples, providing an all inclusive guide to R, ideal for novice and more accomplished users alike. The book assumes no background in statistics or computing and introduces the advantages of the R environment, detailing its applications in a wide range of disciplines.

  • Provides the first comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities.
  • Introduces all the statistical models covered by R, beginning with simple classical tests such as chi-square and t-test.
  • Proceeds to examine more advance methods, from regression and analysis of variance, through to generalized linear models, generalized mixed models, time series, spatial statistics, multivariate statistics and much more.

The R Book is aimed at undergraduates, postgraduates and professionals in science, engineering and medicine. It is also ideal for students and professionals in statistics, economics, geography and the social sciences.


Excerpts from Chapter 4 of The R Book

Chapter 4: Level Set Trees and Code
Learn how to make a volume plot and a barycenter plot, and calculate level set trees with the algorithm LeafsFirst, which is implemented in function ``leafsfirst''. This function takes as an argument a piecewise constant function object.

The multimodal 2D example

(Click on image to enlarge)

We consider the density shown in the 2D three-modal density, and calculate first a piecewise constant function object representing this function, and then calculate the level set tree.

N<-c(35,35)                      # size of the grid pcf<-sim.data(N=N,type="mulmod") # piecewise constant function lst.big<-leafsfirst(pcf)         # level set tree 
We may make the volume plot with the command ''plotvolu(lst)''. However, it is faster first to prune the level set tree, and then plot the reduced level set tree. Function ''treedisc'' takes as the first argument a level set tree, as the second argument the original piecewise constant function, and the 3rd argument ''ngrid'' gives the number of levels in the pruned level set tree. We try the number of levels ngrid=100.
lst<-treedisc(lst.big,pcf,ngrid=100) 

Now we may make a volume plot with the function ''plotvolu''.

plotvolu(lst) 

We draw barycenter plots with the function ''plotbary''.

 plotbary(lst,coordi=2)  # 2nd coordinate 

Note: We may find the number and the location of the modes with the ''modecent'' function, which takes as argument a level set tree. Function ''locofmax'' takes as argument a piecewise constant function and calculates the location of the maximum.

modecent(lst) locofmax(pcf) 

The 3D tetrahedron example

(Click on image to enlarge)

We consider the 3-dimensional example. The calculation is much more time consuming this time.

N<-c(32,32,32)                    # the size of the grid pcf<-sim.data(N=N,type="tetra3d") # piecewise constant function lst.big<-leafsfirst(pcf)             # level set tree lst<-treedisc(lst.big,pcf,ngrid=200) # pruned level set tree plotvolu(lst,modelabel=FALSE)        # volume plot plotvolu(lst,cutlev=0.010,ptext=0.00045,colo=TRUE) # zooming coordi<-1                   # coordinate, coordi = 1, 2, 3 plotbary(lst,coordi=coordi,ptext=0.0006) # barycenter plot  

This time we have used parameter ''cutlev'' to make a zoomed volume plot. When this parameter is given, then only the part of the level set tree is shown which is above the value ''cutlev''. Typically it is better to zoom in to the volume plot by cutting the tails of the volume function away. This is achieved by the parameter ''xlim''. We may us for example the following command to make a ``vertically zoomed'' volume plot.

plotvolu(lst,xlim=c(140,220),ptext=0.00045,          colo=TRUE,modelabel=FALSE)  

Additional parameters which we have used are the ''modelabel'', which is used to suppress the plotting of the mode labels, ''ptext'', which lifts the mode labels with the given amount, and ''colo'', which colors the graph of the volume function to make a comparison with the barycenter plots easier.

The 4D pentahedron example

(Click on image to enlarge)

We consider the 4-dimensional example.

N<-c(16,16,16,16)   pcf<-sim.data(N=N,type="penta4d") lst.big<-leafsfirst(pcf) lst<-treedisc(lst.big,pcf,ngrid=100) plotvolu(lst,modelabel=F)  # volume plot plotvolu(lst,cutlev=0.0008,ptext=0.00039,colo=TRUE) # zooming coordi<-1               # coordinate, coordi = 1, 2, 3, 4 plotbary(lst,coordi=coordi,ptext=0.0003) # barycenter plot 

Review

"It may seem strange to include a new edition of a textbook in the preview, but I feel that this is one that is definitely worth highlighting. It is a classic that does not just sell to students during term time but has a much wider appeal ... This edition will sell really well on publication." (The Bookseller, 16 December 2011)

"There is a tremendous amount of information in the book, and it will be very helpful … .This is a potentially very useful book." (Journal of Applied Science, December 2008)

"…if you are an R user or wannabe R user, this text is the one that should be on your shelf.  The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R." (The American Statistician, Aug 2008)

"The R Book; provides the first comprehensive reference manual for the R language." (Statistica 2008)

"…a 950-page comprehensive reference manual for what is perhaps becoming the most powerful and flexible statistical software environment…" (CHOICE, December 2007)

‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, 19th July 2007)  

"There is a tremendous amount of information in the book, and it will be very helpful … .This is a potentially very useful book." (Journal of Applied Science, Dec 2008)

 


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Customer Reviews

It's comprehensive and relatively easy to follow.
Larry Seligman
The chapter commits an essential sin of technical writing by mentioning key terms many pages (even chapters) before they are properly defined.
Solemn Humor
If you're new to object oriented programming, this book will not teach you how to program in R. Only practice and good example can do that.
Delta1

Most Helpful Customer Reviews

240 of 241 people found the following review helpful By Delta1 on December 8, 2007
Format: Hardcover
This book is both ponderous and expensive, so my decision to buy it was predicated on the dual claim that it's 'the first comprehensive reference manual for the R language' and `ideal for novice and accomplished user alike'. As an R beginner and non-statistician (with some long-ago training therein) pressed into scientific data analysis on a regular basis, I wanted a comprehensive reference that covers both the R language and theory behind modern applied statistical methods.This is no small undertaking, but Crawley succeeds reasonably well at the task.

The book contains 27 chapters. The first 5 chapters cover subjects like getting started, essentials of the R language, data input, data frames, and graphics. A lot of the information in these chapters is freely available online at CRAN, or may be queried from within R itself. Still, I find it useful to have this info as part of any desktop reference, and most books on R are similarly equipped. I found nothing lacking here.

Chapters 6-8 cover tables, mathematics, and classical tests. In the mathematics chapter, you'll be introduced to a wealth of math and probability functions, as well as the basics of matrix algebra. If your statistical training centered mainly on the basic normal, student's t, Fisher's F, poisson, and chi-square distributions, get ready for an education. The author's presentation of this material is both in-depth and well articulated.
Chapters 9-20 cover statistical modeling, regression, ANOVA, ANCOVA, GLM, count data, count data in tables, proportion data, binary response variables, GAMs, non-linear models, and mixed effects models.Chapters 21-26 address more advanced topics of tree models, time series analysis, spatial statistics, multivariate statistics, survival analysis and simulation.
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68 of 68 people found the following review helpful By J. Felipe Ortega Soto on August 30, 2008
Format: Hardcover Verified Purchase
Given the length of this book, and the list of contents covered, I had the highest expectations about it.

After spending 2 intensive months reading it, I have mixed feelings. Positive points are the large number of statistical models and methods described. The R examples are useful to follow the explanations, and the writing style is comprehensive. I agree with some reviewers in that the linear models section (Chaps. 9-19) is the most useful one. The last Chapter also presents useful tricks for dealing with graphs in R.

Unfortunately, I have 2 important complaints. The first one is about the presentation of contents: simply CHAOTIC. The author systematically abuses of cross-references. You will find sentences like "here we present an example of [method XX] that will be introduced on page XXX" throughout the entire book. This is disappointing, since it forces the reader to constantly move back and forth, looking for the relevant info. There is no point in presenting an example based on a method that you haven't introduced yet. Examples should be autonomous, and not frequently taken from previous data sets "already used in page YYY".

The second complaint derives from the previous one. The book is hard to use as both a reference manual and a companion for undergraduate or graduate students. Disregarding the comments from the author, if you don't have a solid theoretical background in statistical inference, regression analysis and linear models, you won't get very much benefit of this book. The author completely lacks of a rigorous, structured method for presenting new concepts. Even worse, important definitions and concepts are usually hidden in between of examples that has nothing to do with them.
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32 of 32 people found the following review helpful By David Winsemius on April 16, 2010
Format: Hardcover Verified Purchase
I have been using R for about 5 years and for the last 2 years have I been using it regularly. This book might have helped at the early stages of learning R, but at the moment I have learned not to trust this text. I am now seeing questions on the r-help mailing list related to the disorganization that others have commented upon.

The problems start early. For Windows users he uses doubled backslashes without explaining why these are needed or the alternative. He gets the distinction between the two indexing operators wrong when he states that one does not use "[" with lists (and fails to note that dataframes are in point of fact lists.). After starting with the R concept of vectors, the author introduces functions which have list arguments before even describing either lists or the list() function. I could go on and on. Going much further on to the section on count data he compares a dataset to the theoretic Poisson distribution noting that the counts at the low and the high ranges are larger than expected, calling this "highly aggregated", whereas most statisticians would call this over-dispersed. In the next paragraph he makes the opposite statement but then says that the overly dispersed data "shows randomness". I was further bother by his frequent use of the dangerous practice of using attach and the confusing practice of naming objects using names that were also function names such as "exp" and "data". I get the sense that the R News reviewer gave up noting errors and decided instead to move quickly on to recommending "Modern Applied Statistics" by Venables and Ripley. I would also suggest Harrell's "Regression Modeling Strategies".
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